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On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Open accessAbstract
In the portion of the paper translated here, Neyman introduces a model for the analysis of field experiments conducted for the purpose of comparing a number of crop varieties, which makes use of a double-indexed array of unknown potential yields, one index corresponding to varieties and the other to plots. The yield corresponding to only one variety will be observed on any given plot, but through an urn model embodying sampling without replacement from this doubly indexed array, Neyman obtains a formula for the variance of the difference between the averages of the observed yields of two varieties. This variance involves the variance over all plots of the potential yields and the correlation coefficient $r$ between the potential yields of the two varieties on the same plot. Since it is impossible to estimate $r$ directly, Neyman advises taking $r = 1$, observing that in practice this may lead to using too large an estimated standard deviation, when comparing two variety means.
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Cited by
Recent Developments in the Econometrics of Program Evaluation
Imbens, G. W., & Wooldridge, J. M. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.1.5 · Cited by 4,926
Identification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230
Causal inference in statistics: An overview
Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336
“Those versed in the potential-outcome notation (Neyman, 1923; Rubin, 1974; Holland, 1988), can recognize such expressions through the subscripts that are attached to counterfactual events and variables, e.g. Yx(u) or Zxy.”
BackgroundMethodCausal Inference Using Potential Outcomes: Design, Modeling, Decisions
Rubin, D. B. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001880 · Cited by 2,173
Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014) · Political Analysis · doi:10.1093/pan/mpt024 · Cited by 1,990
Metadata
- Authors
- Splawa-Neyman, J., Dabrowska, D. M., & Speed, T. P.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1990-11-01
- Volume
- 5
- Issue
- 4
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W1490576214
- Cited by
- 1,627
- References
- 0
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Splawa-Neyman, J., Dabrowska, D. M., & Speed, T. P. (1990). On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9. Statistical Science. https://doi.org/10.1214/ss/1177012031
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.